Hasil untuk "Microbial ecology"

Chuntao Yin, Nathan Lahr

Soybean cyst nematode (SCN), Heterodera glycines Ichinohe, is a serious threat to soybean production worldwide. Genetic resistance and crop rotation are the primary management strategies. However, because of limited genetic resources, long-term implementation of crop rotation, and environmental effects, alternative measures are needed. Plant microbiomes play an important role in plant health, but their contribution to SCN resistance remains unclear. In this study, we profiled the rhizosphere microbiomes in 10 soybean cultivars, including 5 SCN-resistant and 5 SCN-susceptible cultivars, using amplicon sequencing. The resistant cultivars harbored distinct rhizosphere microbiomes, compared with the susceptible cultivars. Permutational multivariate analysis of variance revealed that both the host genotype and SCN resistance trait significantly influenced microbial community composition, with host genotype explaining greater variation. Resistant cultivars were found to be enriched in specific microbial taxa from Phenylobacterium, Pseudoduganella, Comamonadaceae, and Arthrobotrys. Furthermore, microbial inoculants derived from resistant cultivars reduced SCN populations in the susceptible cultivar Williams 82. These results suggest that host genotype and SCN resistance trait interacted to shape rhizosphere microbiomes and influence SCN suppression. Overall, this study highlighted the potential for engineering plant microbiomes to enhance soybean resistance to SCN, complementing traditional crop health improvement practices. [Figure: see text] The author(s) have dedicated the work to the public domain under the Creative Commons CC0 “No Rights Reserved” license by waiving all of his or her rights to the work worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law, 2026.

Anne de Bortoli, Anders Bjorn, Francois Saunier et al.

Purpose : Planning a transition towards sustainable carbon neutrality at the organization level raises several accounting challenges. This paper aims to shed light on key challenges, highlight answers from current accounting standards and guidance, point out potential inconsistencies or limits, and outline potential solutions from the industrial ecology community through systemic environmental assessment tools, such as life cycle assessment (LCA) and environmentally-extended input-output (EEIO) analysis. Results and discussion: We propose a Measure-Reduce-Neutralize-Control sequence allowing organizations to plan their sustainable net-zero strategy, and discuss 24 accounting challenges occurring within this sequence. We then outline ways forward for organizations planning their carbon neutrality trajectory, pointing to existing resources, and for guidelines providers and the industrial ecology communities to address current limitations in the development of future accounting methods and guidelines. Overarching solutions to many accounting issues are to develop comprehensive, open-source, and high-quality life cycle inventory databases, to enable improved dynamic assessments and prospective LCA through integrated assessment models, to refine methods for assessing mineral scarcity and environmental impacts, the supply in some metals being expected to be a bottleneck to the energy transition, and to identify the appropriate climate metrics for planning sustainable carbon neutrality pathways at the organizational level.

Hajnal Gyeviki, Mihály Minkó, Mary Karyda et al.

Balaton Borders translates ecological data from Lake Balaton into ceramic tableware that represents human impact on the landscape, from reedbed reduction to shoreline modification and land erosion. Designed for performative dining, the pieces turn shared meals into multisensory encounters where food and data ceramics spark collective reflection on ecological disruption.

Diego Portalanza, Arianna Acosta-Mejillones, Johnny Alcívar et al.

Mangrove ecosystems are globally significant for their biodiversity and ecosystem services but face persistent threats from invasive species and anthropogenic disturbances. This study investigates the interactions between <i>Cyperus rotundus</i>, a widespread invasive weed, and fungal communities in the mangrove-adjacent wetlands of Isla Santay, Ecuador. Using metagenomic sequencing of the ITS region, we analyzed fungal diversity in samples from an anthropogenically pressured area and a non-impacted site. Results revealed significant differences in microbial assemblages: the rhizosphere sample from the disturbed area exhibited lower fungal richness and was dominated by <i>Magnaporthaceae</i> (9%) and <i>Aureobasidium melanogenum</i> (5%), both associated with stress-tolerant traits. In contrast, the rhizosphere sample from the non-impacted site showed higher species diversity, with <i>Cladosporium dominicanum</i> (62%) and <i>Talaromyces</i> (11%) as dominant endophytic taxa. Principal Coordinates Analysis (PCoA) and co-occurrence networks highlighted distinct fungal partitioning between the two sample tissues, indicating that <i>C. rotundus</i> mediates microbial composition in response to environmental gradients. These findings underscore the role of microbial communities in the plant’s invasive success and suggest that leveraging beneficial fungi could enhance ecosystem resilience and support wetland restoration. By integrating molecular approaches with ecological insights, this work contributes to a deeper understanding of microbial dynamics in coastal wetlands and informs targeted management strategies to preserve mangrove habitats.

Paula Gauvin, Isabelle Domaizon, Agnes Bouchez et al.

ABSTRACT Environmental DNA (eDNA) is revolutionizing biodiversity monitoring, offering a unique approach to assess multi‐taxonomic diversity with various applications related to evaluation, protection, and restoration of aquatic ecosystems. However, there is still a lack of sufficient studies to assess the complementarity of various environmental matrices and their contribution to enhancing biodiversity detection. This study evaluates the impact of eDNA sampling in different matrices to measure biodiversity of different taxonomic groups. We set up a year‐long eDNA sampling of water and biofilm matrices in a large lake littoral zone (Lake Geneva), focusing on microalgae, benthic macroinvertebrates, and fish. We first assessed primer specificity, which was high for microalgae (23S) and fish (12S) but was lower for macroinvertebrates (COI). We then evaluated the complementarity of eDNA signals in water and biofilms. For microalgae, communities from biofilm and water were highly different: water eDNA almost exclusively detected planktonic taxa while biofilm eDNA detected predominantly benthic taxa. For macroinvertebrates, communities in water and biofilms were also different, and biofilm eDNA could detect mostly Chironomidae. Finally, for fish, eDNA of both matrices enabled us to detect similar communities even if a few rare species were detected only in water. In the framework of the assessment of ecosystem quality or restoration actions success, we recommend diversifying matrices to collect eDNA to capture a complete picture of microalgae and macroinvertebrate communities. For fish, it is possible to sample water or biofilms, keeping in mind that water has become a standard practice for fish eDNA sampling.

Lucia Gordon, Nico Lang, Catherine Ressijac et al.

Multimodal aerial data are used to monitor natural systems, and machine learning can significantly accelerate the classification of landscape features within such imagery to benefit ecology and conservation. It remains under-explored, however, how these multiple modalities ought to be fused in a deep learning model. As a step towards filling this gap, we study three strategies (Early fusion, Late fusion, and Mixture of Experts) for fusing thermal, RGB, and LiDAR imagery using a dataset of spatially-aligned orthomosaics in these three modalities. In particular, we aim to map three ecologically-relevant biophysical landscape features in African savanna ecosystems: rhino middens, termite mounds, and water. The three fusion strategies differ in whether the modalities are fused early or late, and if late, whether the model learns fixed weights per modality for each class or generates weights for each class adaptively, based on the input. Overall, the three methods have similar macro-averaged performance with Late fusion achieving an AUC of 0.698, but their per-class performance varies strongly, with Early fusion achieving the best recall for middens and water and Mixture of Experts achieving the best recall for mounds.

Tong Wang, Nannan Zhou, Feifei Ding et al.

Abstract Background Commensal bacteria in the intestine release enzymes to degrade and ferment dietary components, producing beneficial metabolites. However, the regulatory effects of microbial-derived enzymes on the intestinal microbiota composition and the influence on host health remain elusive. Xylanase can degrade xylan into oligosaccharides, showing wide application in feed industry. Results To validate the immune-protective effects of xylanase, Nile tilapia was used as the model and fed with xylanase. The results showed that dietary xylanase improved the survival rate of Nile tilapia when they were challenged with Aeromonas hydrophila. The transcriptome analysis showed significant enrichment of genes related to interleukin-17d (il-17d) signaling pathway in the xylanase treatment group. High-throughput sequencing revealed that dietary xylanase altered the composition of the intestinal microbiota and directly promoted the proliferation of Allobaculum stercoricanis which could produce butyrate in vitro. Consequently, dietary xylanase supplementation increased the butyrate level in fish gut. Further experiment verified that butyrate supplementation enhanced the expression of il-17d and regenerating islet-derived 3 gamma (reg3g) in the gut. The knockdown experiment of il-17d confirmed that il-17d is necessary for butyrate to protect Nile tilapia from pathogen resistance. Flow cytometry analysis indicated that butyrate increased the abundance of IL-17D+ intestinal epithelial cells in fish. Mechanistically, butyrate functions as an HDAC3 inhibitor, enhancing il-17d expression and playing a crucial role in pathogen resistance. Conclusion Dietary xylanase significantly altered the composition of intestinal microbiota and increased the content of butyrate in the intestine. Butyrate activated the transcription of il-17d in intestinal epithelial cells by inhibiting histone deacetylase 3, thereby protecting the Nile tilapia from pathogen infection. This study elucidated how microbial-derived xylanase regulates host immune function, providing a theoretical basis for the development and application of functional enzymes. Video Abstract

Qinglei Sun, Zihao Yuan, Yuanyuan Sun et al.

Abstract Background Vestimentiferan tubeworms are deep-sea colonizers, in which chemoautotrophic symbiosis was first observed. These animals are gutless and depend on endosymbiotic bacteria for organic compound synthesis and nutrition supply. Taxonomically, vestimentiferans belong to Siboglinidae and Annelida. Compared with other siboglinids, vestimentiferans are distinguished by high tolerance of the prevailing hydrogen sulfide in hydrothermal vents, rapid growth in local habitats, and a physical structure consisting of a thick chitinous tube. The metabolic mechanisms contributing to these features remain elusive. Results Comparative genomics revealed that unlike other annelids, vestimentiferans possessed trehaloneogenesis and lacked gluconeogenesis. Transcriptome and metabolome analyses detected the expression of trehalose-6-phosphate synthase/phosphatase (TPSP), the key enzyme of trehaloneogenesis, and trehalose production in vestimentiferan tissues. In addition to trehaloneogenesis, glycogen biosynthesis evidenced by packed glycogen granules was also found in vestimentiferan symbionts, but not in other Siboglinidae symbionts. Data mining and analyses of invertebrate TPSP revealed that the TPSP in Vestimentifera, as well as Cnidaria, Rotifera, Urochordata, and Cephalochordata, likely originated from Arthropoda, possibly as a result of transposon-mediated inter-phyla gene transfer. Conclusion This study indicates a critical role of bacterial glycogen biosynthesis in the highly efficient symbiont − vestimentiferan cooperation. This study provides a new perspective for understanding the environmental adaptation strategies of vestimentiferans and adds new insights into the mechanism of metabolic evolution in Metazoa. Video Abstract

Mario Figueira-Pereira, Xavier Barber, David Conesa et al.

In ecology we may find scenarios where the same phenomenon (species occurrence, species abundance, etc.) is observed using two different types of samplers. For instance, species data can be collected from scientific sampling with a completely random sample pattern, but also from opportunistic sampling (e.g., whale or bird watching fishery commercial vessels), in which observers tend to look for a specific species in areas where they expect to find Species Distribution Models (SDMs) are a widely used tool for analyzing this kind of ecological data. Specifically, we have two models available for the above data: a geostatistical model (GM) for the data coming from a complete random sampler and a preferential model (PM) for data from opportunistic sampling. Integration of information coming from different sources can be handled via expert elicitation and integrated models. We focus here in a sequential Bayesian procedure to connect two models through the update of prior distributions. Implementation of the Bayesian paradigm is done through the integrated nested Laplace approximation (INLA) methodology, a good option to make inference and prediction in spatial models with high performance and low computational costs. This sequential approach has been evaluated by simulating several scenarios and comparing the results of sharing information from one model to another using different criteria. The procedure has also been exemplified with a real dataset. Our main results imply that, in general, it is better to share information from the independent (completely random) to the preferential model than the alternative way. However, it depends on different factors such as the spatial range or the spatial arrangement of sampling locations.

Maitham G. Yousif

In this research, medical information from 1200 patients across various hospitals in Iraq was collected over a period of 3 years, from February 3, 2018, to March 5, 2021. The study encompassed several infections, including urinary tract infections, wound infections, tonsillitis, prostatitis, endometritis, endometrial lining infections, burns infections, pneumonia, and bloodstream infections in children. Multiple bacterial pathogens were identified, and their resistance to various antibiotics was recorded. The data analysis revealed significant patterns of antibiotic resistance among the identified bacterial pathogens. Resistance was observed to several commonly used antibiotics, highlighting the emerging challenge of antimicrobial resistance in Iraq. These findings underscore the importance of implementing effective antimicrobial stewardship programs and infection control measures in healthcare settings to mitigate the spread of antibiotic-resistant infections and ensure optimal patient outcomes. This study contributes valuable insights into the prevalence and patterns of antibiotic resistance in microbial infections, which can guide healthcare practitioners and policymakers in formulating targeted interventions to combat the growing threat of antimicrobial resistance in Iraq's healthcare landscape.

Edgar Santos-Fernandez, Julie Vercelloni, Aiden Price et al.

Crowdsourcing methods facilitate the production of scientific information by non-experts. This form of citizen science (CS) is becoming a key source of complementary data in many fields to inform data-driven decisions and study challenging problems. However, concerns about the validity of these data often constrain their utility. In this paper, we focus on the use of citizen science data in addressing complex challenges in environmental conservation. We consider this issue from three perspectives. First, we present a literature scan of papers that have employed Bayesian models with citizen science in ecology. Second, we compare several popular majority vote algorithms and introduce a Bayesian item response model that estimates and accounts for participants' abilities after adjusting for the difficulty of the images they have classified. The model also enables participants to be clustered into groups based on ability. Third, we apply the model in a case study involving the classification of corals from underwater images from the Great Barrier Reef, Australia. We show that the model achieved superior results in general and, for difficult tasks, a weighted consensus method that uses only groups of experts and experienced participants produced better performance measures. Moreover, we found that participants learn as they have more classification opportunities, which substantially increases their abilities over time. Overall, the paper demonstrates the feasibility of CS for answering complex and challenging ecological questions when these data are appropriately analysed. This serves as motivation for future work to increase the efficacy and trustworthiness of this emerging source of data.

Aymeric Vie, J. Doyne Farmer

Agent-based models (ABMs) are fit to model heterogeneous, interacting systems like financial markets. We present the latest advances in Evology: a heterogeneous, empirically calibrated market ecology agent-based model of the US stock market. Prices emerge endogenously from the interactions of market participants with diverse investment behaviours and their reactions to fundamentals. This approach allows testing trading strategies while accounting for the interactions of this strategy with other market participants and conditions. Those early results encourage a closer association between ABMs and ML algorithms for testing and optimising investment strategies using machine learning algorithms.

Hao-Yu Lo, Konstantin Wink, Henrike Nitz et al.

ABSTRACT Current methods for the identification of specific microorganisms based on an in situ metabolism are often hampered by insufficient sensitivity and habitat complexity. Here, we present a novel approach for identifying and sequencing single microbial cells metabolizing a specific organic compound with high sensitivity and without prior knowledge of the microbial community. The workflow consists of labeling individual cells with a [14C] substrate based on their metabolic activity, followed by encapsulating cells in alginate with nuclear emulsion by using microfluidics. We here adapted the concept of microautoradiography to visually distinguish between encapsulated labeled and non-labeled cells, which are then sorted via flow cytometry for single cell genomics. As a proof-of-concept, we labeled, separated, lysed, and sequenced single cells of the benzene degrader Pseudomonas veronii from mock microbial communities. The cells of P. veronii were isolated with 100% specificity. Single-cell microautoradiography and genome sequencing is an innovative method for elucidating microbial identity, activity, and function in diverse habitats, contributing to elucidate novel taxa and genes with potential for biotechnological applications such as bioremediation.IMPORTANCEA central question in microbial ecology is which member of a community performs a particular metabolism. Several sophisticated isotope labeling techniques are available for analyzing the metabolic function of populations and individual cells in a community. However, these methods are generally either insufficiently sensitive or throughput-limited and thus have limited applicability for the study of complex environmental samples. Here, we present a novel approach that combines highly sensitive radioisotope tracking, microfluidics, high-throughput sorting, and single-cell genomics to simultaneously detect and identify individual microbial cells based solely on their in situ metabolic activity, without prior information on community structure.

Ashish B. George, James O'Dwyer

The growth of complex populations, such as microbial communities, forests, and cities, occurs over vastly different spatial and temporal scales. Although research in different fields has developed detailed, system-specific models to understand each individual system, a unified analysis of different complex populations is lacking; such an analysis could deepen our understanding of each system and facilitate cross-pollination of tools and insights across fields. Here, for the first time we use a shared framework to analyze time-series data of the human gut microbiome, tropical forest, and urban employment. We demonstrate that a single, three-parameter model of stochastic population dynamics can reproduce the empirical distributions of population abundances and fluctuations in all three data sets. The three parameters characterizing a species measure its mean abundance, deterministic stability, and stochasticity. Our analysis reveals that, despite the vast differences in scale, all three systems occupy a similar region of parameter space when time is measured in generations. In other words, although the fluctuations observed in these systems may appear different, this difference is primarily due to the different physical timescales associated with each system. Further, we show that the distribution of temporal abundance fluctuations is described by just two parameters and derive a two-parameter functional form for abundance fluctuations to improve risk estimation and forecasting.

Pritam Banerjee, Kathryn A. Stewart, Caterina M. Antognazza et al.

Abstract Plant–animal interactions (PAI) represent major channels of energy transfer through ecosystems, where both positive and antagonistic interactions simultaneously contribute to ecosystem functioning. Monitoring PAI therefore increases the understanding of environmental health, integrity, and functioning, and studying complex interactions through accurate, cost‐effective sampling can aid in the management of detrimental anthropogenic impacts. Environmental DNA (eDNA)‐based monitoring represents an increasingly common, nondestructive approach for biodiversity monitoring, which could help to elucidate PAI. Here, we aim to provide an overall discussion on the potential of using eDNA to study PAI. We assessed the existing literature on this subject from 2009 to 2021 using a freely accessible web search tool. The search was conducted by using keywords involving eDNA and PAI, including both species‐specific and metabarcoding approaches, recovering 43 studies. We summarized the advantages and current limitations of such approaches, and we outline research priorities to improve future eDNA‐based methods for PAI analysis. Among the 43 studies identified using eDNA to measure PAI such as pollination, herbivory, mutualistic, and parasitic relationships, they have often identified higher taxonomic diversity in several direct comparisons with DNA‐based gut/bulk sampling and conventional survey methods. Research needs include the following: better understanding of the influencing factors of eDNA detection involved in PAI (e.g., eDNA degradation, origin, and types), methodological standardization (sampling methods and primer development), and more inclusive sequence reference databases. If these research priorities are addressed, it will have a significant impact to enable PAI biodiversity monitoring with eDNA. In the future, the implementation of eDNA methods to study PAI can particularly benefit the scalability of environmental biomonitoring surveys that are imperative for ecosystem health assessments.

Pu-Dong Li, Zeng-Rong Zhu, Yunzeng Zhang et al.

Abstract Background Plants can recruit beneficial microbes to enhance their ability to defend against pathogens. However, in contrast to the intensively studied roles of the rhizosphere microbiome in suppressing plant pathogens, the collective community-level change and effect of the phyllosphere microbiome in response to pathogen invasion remains largely elusive. Results Here, we integrated 16S metabarcoding, shotgun metagenomics and culture-dependent methods to systematically investigate the changes in phyllosphere microbiome between infected and uninfected citrus leaves by Diaporthe citri, a fungal pathogen causing melanose disease worldwide. Multiple microbiome features suggested a shift in phyllosphere microbiome upon D. citri infection, highlighted by the marked reduction of community evenness, the emergence of large numbers of new microbes, and the intense microbial network. We also identified the microbiome features from functional perspectives in infected leaves, such as enriched microbial functions for iron competition and potential antifungal traits, and enriched microbes with beneficial genomic characteristics. Glasshouse experiments demonstrated that several bacteria associated with the microbiome shift could positively affect plant performance under D. citri challenge, with reductions in disease index ranging from 65.7 to 88.4%. Among them, Pantoea asv90 and Methylobacterium asv41 identified as “recruited new microbes” in the infected leaves, exhibited antagonistic activities to D. citri both in vitro and in vivo, including inhibition of spore germination and/or mycelium growth. Sphingomonas spp. presented beneficial genomic characteristics and were found to be the main contributor for the functional enrichment of iron complex outer membrane receptor protein in the infected leaves. Moreover, Sphingomonas asv20 showed a stronger suppression ability against D. citri in iron-deficient conditions than iron-sufficient conditions, suggesting a role of iron competition during their antagonistic action. Conclusions Overall, our study revealed how phyllosphere microbiomes differed between infected and uninfected citrus leaves by melanose pathogen, and identified potential mechanisms for how the observed microbiome shift might have helped plants cope with pathogen pressure. Our findings provide novel insights into understanding the roles of phyllosphere microbiome responses during pathogen challenge. Video abstract

Ulas Karaoz, Eoin L. Brodie, Eoin L. Brodie

Remote sensing approaches have revolutionized the study of macroorganisms, allowing theories of population and community ecology to be tested across increasingly larger scales without much compromise in resolution of biological complexity. In microbial ecology, our remote window into the ecology of microorganisms is through the lens of genome sequencing. For microbial organisms, recent evidence from genomes recovered from metagenomic samples corroborate a highly complex view of their metabolic diversity and other associated traits which map into high physiological complexity. Regardless, during the first decades of this omics era, microbial ecological research has primarily focused on taxa and functional genes as ecological units, favoring breadth of coverage over resolution of biological complexity manifested as physiological diversity. Recently, the rate at which provisional draft genomes are generated has increased substantially, giving new insights into ecological processes and interactions. From a genotype perspective, the wide availability of genome-centric data requires new data synthesis approaches that place organismal genomes center stage in the study of environmental roles and functional performance. Extraction of ecologically relevant traits from microbial genomes will be essential to the future of microbial ecological research. Here, we present microTrait, a computational pipeline that infers and distills ecologically relevant traits from microbial genome sequences. microTrait maps a genome sequence into a trait space, including discrete and continuous traits, as well as simple and composite. Traits are inferred from genes and pathways representing energetic, resource acquisition, and stress tolerance mechanisms, while genome-wide signatures are used to infer composite, or life history, traits of microorganisms. This approach is extensible to any microbial habitat, although we provide initial examples of this approach with reference to soil microbiomes.

Luciana P. Di Salvo, Gabriel C. Cellucci, M. Carlino et al.

Abstract Plant growth-promoting rhizobacteria (PGPR) were used as inoculants of cereal crops to improve their growth and grain yield. The crops responses to inoculation are complex because are defined by plant-microorganisms interactions, many of them still unknown. Thus, it is necessary to improve the knowledge about the microbial ecology of the rhizosphere of crops under different agricultural practices. The aim of this study was to evaluate the effects of certain PGPR inoculants and nitrogen fertilization on maize (Zea mays L.) production and some associated microbial communities under field conditions in order to increase the knowledge about microbial ecology to improve crop response to PGPR inoculation. A field experiment of maize was performed to evaluate five PGPR inoculation treatments -including commercial and experimental inoculants of Azospirillum brasilense or Pseudomonas fluorescens- and three levels of nitrogen fertilization. Particular microbial groups belonging to the carbon and nitrogen soil cycles were analyzed. Nitrogen fertilization and PGPR inoculation increased maize grain yield. Inoculation only modified the number of microaerophilic nitrogen fixing (MNF) microorganisms at the reproductive stage of the crop, while fertilization modified the amount of cellulolytic, nitrifying and MNF microorganisms, only in the vegetative stage of maize. In addition, it was observed that both inoculation and fertilization modified the physiology of the rhizosphere microbial communities in the reproductive stage. Physiological changes observed in different ontogenetic stages of the crop had higher impact than both agricultural practices. All the results demonstrate that changes in the relationships between plant and microorganisms are due to different management decisions. This work gives a better understanding of maize-rhizosphere microbial ecology which can be used to improve PGPR inoculation response in order to obtain a sustainable agricultural production.

Sudeepto Bhattacharya

A social-ecological network is a formal representation of a corresponding social-ecological system, and encodes a relation within a given system as an interaction. Conventionally, such networks have been defined as encoding and representing pairwise interactions among the fundamental units of the system. This work proposes a combinatorial definition of social-ecological network by means of its structure as a simplicial complex. The proposed definition is a comprehensive one that takes into account the heterogeneity of interactions within a given SES, and the higher-order social-ecological network modelled using this definition is able to represent the modelled SES by capturing all orders of interactions within the system. Such a social-ecological network consequently, is better equipped to capture and represent the structural details of the real-world SES, and is thus capable of facilitating a deeper insight into the complex behaviour of the represented SES emergent through the higher-order interactions within the system, as compared to the conventional graph-theoretic network that exclusively models pairwise interactions.

Aurélien Maillet, Pauline Denojean, Agnès Bouju-Albert et al.

Cold-smoked salmon is a widely consumed ready-to-eat seafood product that is a fragile commodity with a long shelf-life. The microbial ecology of cold-smoked salmon during its shelf-life is well known. However, to our knowledge, no study on the microbial ecology of cold-smoked salmon using next-generation sequencing has yet been undertaken. In this study, cold-smoked salmon microbiotas were investigated using a polyphasic approach composed of cultivable methods, V3—V4 16S rRNA gene metabarcoding and chemical analyses. Forty-five cold-smoked salmon products processed in three different factories were analyzed. The metabarcoding approach highlighted 12 dominant genera previously reported as fish spoilers: Firmicutes <i>Staphylococcus, Carnobacterium, Lactobacillus,</i> β-Proteobacteria <i>Photobacterium, Vibrio, Aliivibrio, Salinivibrio, Enterobacteriaceae Serratia,</i><i>Pantoea</i>, γ-Proteobacteria <i>Psychrobacter, Shewanella</i> and <i>Pseudomonas</i>. Specific operational taxonomic units were identified during the 28-day storage study period. Operational taxonomic units specific to the processing environment were also identified. Although the 45 cold-smoked salmon products shared a core microbiota, a processing plant signature was found. This suggest that the bacterial communities of cold-smoked salmon products are impacted by the processing environment, and this environment could have a negative effect on product quality. The use of a polyphasic approach for seafood products and food processing environments could provide better insights into residential bacteria dynamics and their impact on food safety and quality.